Search Results for author: Maher Nouiehed

Found 8 papers, 1 papers with code

Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods

1 code implementation NeurIPS 2019 Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D. Lee, Meisam Razaviyayn

In this paper, we study the problem in the non-convex regime and show that an \varepsilon--first order stationary point of the game can be computed when one of the player's objective can be optimized to global optimality efficiently.

Rényi Fair Inference

no code implementations ICLR 2020 Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, Meisam Razaviyayn

In this paper, we use R\'enyi correlation as a measure of fairness of machine learning models and develop a general training framework to impose fairness.

BIG-bench Machine Learning Clustering +2

Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances

no code implementations15 Jun 2020 Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong

The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games.

SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization

no code implementations10 Nov 2020 Xubo Yue, Maher Nouiehed, Raed Al Kontar

In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.

Scheduling

GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

no code implementations5 Aug 2021 Xubo Yue, Maher Nouiehed, Raed Al Kontar

In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings.

Fairness Federated Learning +1

SALR: Sharpness-aware Learning Rates for Improved Generalization

no code implementations28 Sep 2020 Xubo Yue, Maher Nouiehed, Raed Al Kontar

In an effort to improve generalization in deep learning, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.

SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection

no code implementations12 Oct 2023 Xiaoyang Song, Wenbo Sun, Maher Nouiehed, Raed Al Kontar, Judy Jin

Current techniques for Out-of-Distribution (OoD) detection predominantly rely on quantifying predictive uncertainty and incorporating model regularization during the training phase, using either real or synthetic OoD samples.

Data Augmentation Out-of-Distribution Detection +1

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